The effect of product portfolio greening strategies on legitimacy granted

by Main Street and Wall Street in the automotive industry

University of Groningen

Faculty of Economics and Business

MSc BA Strategic Innovation Management

Mark Schooneman – S2682923

Supervisor:

Prof. Dr. J. Surroca

Co-assessor:

Dr. P. Steinberg

January 20, 2020

Word count: 15213

(including references and appendix)

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Abstract

This study examines the effect of green firm strategies in the automotive industry to secure the

legitimacy on the stakeholder legitimacy granted by two stakeholder groups, the general public (“Main

Street”) and the investors (“Wall Street”). Two product portfolio greening strategies have been

identified: restructuring and extending. Financial data from renowned databases on 13 global car

manufacturing companies from 2006 through 2019 are combined with publicly available data in a panel

data set. The data set yields measures on the sentiment of Main Street, the sentiment of Wall Street and

9 control variables. A fixed effects regression model was fit to the data. The results show no effect of

greening strategy on legitimacy granted by either Main Street or Wall Street. The control variables show

an effect of age, performance and marketing intensity of the firm on the sentiment of Main Street.

Furthermore, an effect is found of performance and R&D intensity on the firm on the sentiment of Wall

Street. From these results it is concluded that the partial greening of the product portfolio of a firm,

irrespective of the applied strategy, is not rewarded with legitimacy by Main Street and Wall Street.

Furthermore, the data suggests that both Main Street and Wall Street have a static or polarized view on

car manufacturers being either green or brown.

Keywords: Legitimacy, Wall Street, Main Street, greening strategies, product portfolio restructuring,

product portfolio extending, automotive industry

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ACKNOWLEDGEMENT

First and foremost, I want to express my gratitude towards my supervisor, J. Surroca, who provided me

with valuable support, motivation and feedback along the road of completing this project. Also, I want

to thank all the people involved in the SIM master. Their efforts and inspiration have made the

realization of this thesis possible. Furthermore, I want to thank my fellow students with whom I have

been able to share my thoughts and struggles with.

Finally, I am thankful for my family and friends who have been there for me throughout the writing of

this thesis.

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TABLE OF CONTENTS

1. INTRODUCTION......................................................................................................................... 5

2. EMPIRICAL SETTING ............................................................................................................... 7

3. THEORETICAL FRAMEWORK ............................................................................................... 8

3.1 Institutional theory & legitimacy ............................................................................................... 8

3.2 Audiences ................................................................................................................................. 9

3.3 Green strategies: greening the product portfolio ....................................................................... 10

3.3.1 Restructuring the product portfolio ................................................................................... 11

3.3.2 Extending the product portfolio ........................................................................................ 11

3.4 Hypotheses.............................................................................................................................. 12

4. METHODOLOGY...................................................................................................................... 14

4.1 Sample and data sources .......................................................................................................... 14

4.2 Measurement of the variables .................................................................................................. 15

4.3 Technique of the analysis ........................................................................................................ 19

5. RESULTS .................................................................................................................................... 20

5.1 Descriptive statistics and correlations ...................................................................................... 20

5.2 Regression results ................................................................................................................... 28

6. DISCUSSION .............................................................................................................................. 31

6.1 Theoretical implications .......................................................................................................... 31

6.2 Managerial implications .......................................................................................................... 33

6.3 Conclusion .............................................................................................................................. 33

6.4 Limitations and future research................................................................................................ 34

REFERENCES ............................................................................................................................... 36

APPENDICES................................................................................................................................. 41

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1. INTRODUCTION

Throughout the years, awareness about the environmental sustainability increased. News regarding

climate change and environmental pollution have reached many people. As a result of this, a growing

concern about the impact of behavior on the environment (Krause, 1993) can be noted. This made actors

at all levels of the market, consumers, governments and business organizations willing to undertake

action. One of these actions is a shift in consumer preferences. Gradually, consumer preferences are

changing from non-green or brown products to green products today (Martin & Simintiras, 1995; Saxena

& Khandelwal, 2008). Businesses respond to this accordingly by inducing corporate ecological

responsiveness through adjusting their product portfolio towards a more green portfolio in the hope to

gain legitimacy and competitiveness (Bansal & Roth, 2000). However, do businesses truly gain from

this?

A product can be labeled ‘green’ when it, consumes less resources when developed or used. This is

reflected in the definition used in academic literature, where green products have been coined as

products “that will not pollute the earth or deplore natural resources, and can be recycled or

conserved” (Shamdasani, Chon-Lin & Richmond, 1993). There are two options in converting the brown

product portfolio into a greener one. The first option is to restructure the current brown product portfolio

by redesigning or substituting (successful) brown products for green products. The second is to extend

the existing brown product portfolio through the introduction of new green products. In the business

environment, innovation is a necessity for firm survival. According to Porter (1985), being one of the

first companies in an industry to change, in this case towards green products, provides these businesses

a sustainable competitive advantage. However, where green products for some entrepreneurs come as

an opportunity, it can come as a threat to others. To dodge this threat and survive businesses are required

to introduce green products to complement their product portfolio (Yenipazarli & Vakharia, 2015).

However, a shift towards a green(er) product portfolio must be made carefully, as formerly brown

companies are vulnerable to accusations of greenwashing (Delmas & Burbano, 2011).

In keeping up with customer demand and competition, and concurrently doing good for the environment,

company’s overarching objective is to gain in legitimacy. Legitimacy is crucial to firms, because it leads

to the acceptance of the firm by society (Hannah & Freeman, 1984) and it is related to financial

performance as it attracts customers (Rao, Chandy, & Prabhu, 2008). Legitimacy can be granted by

various stakeholders. In this study the stakeholders of concern are the general public (in this study

referred to as “Main Street”) and investors (“Wall Street”). These two stakeholders are chosen, because

research has shown that general public and investors respond to phenomena differently (Lamin &

Zaheer, 2012). By this it can be expected that the two stakeholder groups respond dissimilar to the

product portfolio greening strategies firms can implement. Therefore, firms should take the various

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interests of stakeholders into consideration (Jugend, da Silva, Salgado, & Miguel, 2016) when

implementing greening strategies to gain legitimacy from either one or both of them.

As environmental sustainability became a prominent topic in the public debate, it raised the attention of

researchers related to business, resulting in an increase in studies regarding the topic. However, some

themes within the fields of business have not been investigated yet. Considering the field of

sustainability and product portfolio some interesting researches have been conducted Khalili-Damghani

and Tavana (2014) tested an integrated project portfolio selection approach for strategic and sustainable

projects. Jugend et al. (2017) explored how product portfolio and new product development (NPD) are

influenced by green and traditional practices of NPD. While Yenipazarli and Vakharia (2017) provided

insights considering a firm’s green strategy, taking pricing, environmental benefits and economic return

into account. However, to the best of my knowledge, the degree to which the greening of a product

portfolio contributes to the enhancement or diminution of a firm’s legitimacy, has not been studied. By

the same token, the legitimacy granting audiences of Wall Street and Main Street have not been

discussed in this vein of greening strategy research. Legitimacy is a highly important factor for a firm’s

continuity in the automotive industry (Rao et al., 2008). Therefore, the unique contribution of my

research will be based on this void of studies on legitimacy and investigated on data from the automotive

industry.

One industry whose products and processes have always been a significant source of environmental

impact is the automotive industry. Therefore, this paper focuses on the automotive industry setting. This

will show that when a car manufacturer introduces a green product to its portfolio, it is pivotal to have

a thorough understanding of how Main Street’s and Wall Street’s different perceptions of greening

strategies will influence the legitimacy each strategy grants to the firm. The strategies investigated in

this particular industry are the introduction of greener alternatives (hybrid, electric and fuel-cell powered

engines) by virtue of product portfolio extending and restructuring. Due to time constraints and

unavailability of data, other strategies car manufacturers have adopted to green their products are not

incorporated in this study. The results of this study will enrich the automotive industry’s product

portfolio management problem by providing guidance to business managers in choosing the best

portfolio greening strategy considering the consequences it will have on the legitimacy granted by the

stakeholders of Wall Street and Main Street.

In addressing this, the following thesis is leading:

What is the effect of adding green products to a brown product portfolio through different strategies

on legitimacy granted by stakeholders in the automotive industry?

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The analysis done in the current study finds no evidence for the effect of greening strategies on

legitimacy granted by either Main Street or Wall Street. Hence, none of the assumptions about the

automotive industry are proven wrong or right.

In the subsequent sections of this paper, the first chapter presents the empirical setting regarding the

automotive industry. Second, theoretical framework is outlined which introduces institutional theory

and legitimacy to start with. Succeeding, the two audiences under study are acquainted together with

their varying demands and needs. Consecutive, the two green strategies of product portfolio greening

are further defined. Finally, the hypotheses under study are presented. After this, the third chapter will

present the sample along with the methodological decisions and measures considering the variables of

data gathering are described. Fourth, an analysis of the gathered data is conducted and results from the

analysis are provided. The fifth chapter named conclusion presents the theoretical and managerial

implications of this study and an overall conclusion. Lastly, a discussion in which the limitations and

recommendations for future research are discussed is presented.

2. EMPIRICAL SETTING

Although the ‘modern car’ was born by the filing of the Benz Patent-Moterwagen in 1886, cars came

into global use in the 20th century. Cars have become crucial to developed economies, which is reflected

in the size of the industry. The automotive industry is one of the world’s largest economic sectors by

revenue with an output of 96.9 million vehicles in 2017 (OICA) and a total value of as much as 2 trillion

US dollars (Jenkins, 2018). Because of this nature and the scale, the automotive industry has been

involved in scandals and has been the target of scrutiny. Well-known scandals are the unsafe 1960

Chevrolet Corvair and Ford Pinto from the 1970’s due to respective design errors and cost cuts. The

bribing of officials around the world by Daimler which came to the light in 2010. And the most recent

and arguably biggest environmental scandal of all time: the Volkswagen diesel scandal. Harming not

only the entire automotive industry, but also the segment related to diesel powered engines, like diesel

fueled powerplants.

Apart from the scandals, the environmental impact caused by the industry is a point of debate. Not only

the manufacturing process, but also the use of the vehicles has a large impact (Mildenberger & Khare,

2000) caused by the production of fuel and exhaust emissions during operation of vehicles. The harmful

gasses emitted include carbon dioxide, carbon monoxide, sulfur and nitrogen oxides. These gasses are

emitted by most vehicles, because they are being propelled by internal combustion engines which

consume fossil fuels, such as diesel and gasoline. According to National Geographic (2019), the gasses

emitted on the road by a vehicle make up for 80 to 90 percent of its entire environmental impact.

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Around the turn of the millennium the automotive industry realized they had to adjust their businesses

to the current demand of tailpipe pollution reduction. Examples of adjustments of the industry are the

arrival of the first commercially available hybrid vehicles like the Toyota Prius and the appearance of

electric car manufacturer Tesla Motors, Inc. to the market in 2003. These archetypes show the

implementation of environmentally friendlier and greener products, as they are powered by clean and

renewable electricity.

There is a large global demand for cars and an increasing awareness of the relevance of less polluting

cars (Randall, 2016). As a result, this combination raised a competitive force to green product portfolios

by investing billions of dollars in alternative fuels (Bos & Hsu, 2019). However, well-known

manufacturers might want to take into account that scandals regarding emissions have affected people’s

trust in the existing automotive Industry (Barney & Hansen, 1994). As a result of these two different

forces it remains unclear what strategy is best to adopt. The hypotheses of this research are based on this

setting of not knowing what strategy is best to follow when firms want to satisfy either or both of the

stakeholder groups.

3. THEORETICAL FRAMEWORK

In this section it will be theorized how the audiences of Main Street and Wall Street respond to different

strategies firms implement in shifting towards a greener product portfolio. Ultimately, the audiences

determine the legitimacy they award to a firm based on, among others, their liking of the chosen portfolio

greening strategies. The first section of the theoretical framework will address the topic of legitimacy.

The definition of legitimacy is discussed together with the importance of legitimacy to firms. The second

part presents the relevant stakeholder groups and how they expect firms to operate in order to be awarded

with legitimacy. Third, different product portfolio greening strategies will be described. Lastly, it will

be hypothesized how the stakeholder groups in the current research are likely to respond to the greening

strategies according to related and previous research.

3.1 Institutional theory & legitimacy

Institutional theory says that firms are ingrained in institutional environments. The stakeholders in such

environments have particular expectations of the firms. Firms are anticipated to act upon these

expectations (Boxenbaum & Jonsson, 2008). Freeman (1984) has defined a stakeholder to be a “group

or individual who can affect or is affected by achievement of the organization’s objective”. The

expectations of these stakeholders are based on values and norms, providing a normative basis for

legitimacy, additional to the normative regulative and cognitive ones (Scott, 2013). This study focuses

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on the normative pillar of legitimacy. This is chosen since stakeholder groups each have their own norms

and values to which a firm adjusts its actions. This is a social obligation compared to the regulative and

shared understanding that are at the foundation of respectively the regulative and cultural-cognitive

pillar (Scott, 2013).

Most scholars that write on legitimacy take stance with Suchman’s (1995) definition of the subject.

Within this definition the focal point of legitimacy is the acceptation of firm activities and goals by

different audiences. However, for the context of this study the widely accepted definition of corporate

environmental legitimacy of Bansal and Clelland (2004) is more suitable, since it focusses on one

stakeholder dimension, namely the natural environment. Bansal and Clelland (2004) have defined this

as the generalized perception or assumption that if a firm’s corporate environmental performance is

desirable, proper, or appropriate, is assessed by stakeholders. These stakeholders include managers,

customers, investors, and community members and grant “the firm’s legitimacy according to their own

distinct and diverse norms, “cognitive maps,” and pragmatic preferences” (Bansal & Clelland, 2004).

The current work focuses on a subset of activities of environmental legitimacy, being green legitimacy.

Achieving, maintaining or repairing legitimacy is fundamental for any firm in that it helps to attract

customers (Rao et al., 2008) and thereby boost sales, it can secure governmental protection (Aldrich &

Fiol, 1994) and creates access to capital and (therefore) resources and new markets. In all, this ensures

a firm’s continuity, making legitimization an essential good to any firm. Therefore, any attack on firm

legitimacy should be countered, considering the fact that a destabilization of it can threaten its very

survival (Dowling & Pfeffer, 1975)

3.2 Audiences

Stakeholder groups are the parties who grant the legitimacy to firms who green their product portfolio.

In the field of business, there are numerous stakeholders at play. Stakeholder theory accounts for the

parties any firm operating its business encounters. There are two audiences that will be at the focus of

this study. First, there are investors and shareholders, which are referred to as “Wall Street”. The second,

which includes the non-shareholder stakeholder groups of consumers, communities and media, is the

public at large. For this reason, this second audience is referred to as “Main Street” (Lamin & Zaheer,

2012). The Wall Street and the Main Street stakeholders form divergent understandings about the same

phenomena based on “their assessment of the role of the corporation in society” (Lamin & Zaheer,

2012). This results from the fact that these two stakeholder groups belong to different “thought worlds”.

Each stakeholder group filters and processes information in its own way, depending on its thought world

(Dougherty, 1992). Each different filtering results in a different interpretation of information from the

environment. In the words of Dougherty (1992, p. 182) herself: “a thought world is a community of

persons engaged in a certain domain of activity, who have a shared understanding about that activity.”

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From this it can be explained why different stakeholder groups form dissimilar opinions on the same

phenomena.

In the corporate system of publicly traded stocks and stake investments, Wall Street is a dominant and

determining stakeholder in many business decisions. Wall Street is dominant since they are partially the

owners of the firms and determining as they grant legitimacy to the firm. Leading in the allotment of

legitimacy by investors is “the long-run value of the firm and its future performance as reflected in its

stock price” (Lamin & Zaheer, 2012). Correlated to a firm’s profits is the stock price, thus investor’s

return on investment. Therefore, shareholders are concerned with a firm’s legitimacy residing within

customers too, since this is related to sales and firm profit. Consequently, shareholders monitor a firm’s

actions very closely since these might contribute to an increase or decrease of future cash flows. (Brealey

& Myers 1984; Benner & Ranganathan 2009). To illustrate, when a firm is planning to implement

greening strategies that might result in uncertain or decreased profits, this could potentially lower the

stock price, thus the legitimacy granted to the firm by shareholders will be affected negatively.

Shareholders then want the firm to adhere to them inconsiderate of the positive effects of the new

strategy for society. According to investor’s, privileging the stockholders above the stakeholders is the

appropriate role of the firm in society (Friedman 1962). Evan and Freeman (1988) noted that

stakeholders make different claims on an organization. This discrepancy of interests between

shareholders and non-shareholders becomes rather clear by the fact that firm actions which Wall Street

perceives as positive, may be viewed upon with disapproval by non-shareholders. When, as an

illustration, a river is contaminated due to a firm’s actions, non-shareholders are concerned about the

effects on human health and nature, where shareholders are more interested in the resulting legal and

thereby financial liabilities (P. Bansal & Clelland, 2004). According to Frank (1988) non-shareholders

value a firm’s actions from the perspective of the broader societal impact, instead of the impact of the

financial returns (Beauchamp et al., 2008). Furthermore, the general public evaluates a firm’s actions

based on Suchman’s (1995, p. 574) principles of legitimacy whether “the actions of the firm are

desirable, proper, or appropriate”. This evaluation can be viewed as a social judgement (DiMaggio and

Powell, 1991) determining if a firm qualifies as a good corporate citizen that should be rewarded with

legitimacy. Coming back to the case of the contaminated river. When the accountable firm takes

responsibility to deal with the aftermath by solving the harm done to people and nature combined with

the installment of measures to prevent this from happening again, the legitimacy granted to the firm by

non-shareholders will be affected positively. Hence, for a firm to increase its legitimization with the

stakeholder groups of Main Street and Wall Street, it is imperative to define and adhere to their varying

demands, as Main Street and Wall Street grant legitimacy accordingly to the fulfillment of their needs.

3.3 Green strategies: greening the product portfolio

The marketplace is ever evolving, causing all kinds of changes in the demands of a firm’s stakeholders.

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For a firm to hold on to the legitimacy it has been granted by their stakeholders it is important to move

along with the stakeholders’ changing demands. A firm must undertake actions to maintain, boost or

restore its legitimacy. These actions are driven by strategic choices.

Many incumbent firms are facing a decline in the legitimacy of their conventional, often polluting,

technologies (Patala, et al., 2019). So is the automotive industry whose products are mainly powered by

the usage of fossil fuels such as gasoline or diesel. Industries like this cannot turn away from their

conventional brown products to an entire green product portfolio overnight, but they can transform their

product portfolio in such a manner that it becomes less brown, or put differently, greener. This can be

achieved through incremental innovations, such as increasing efficiency with hybrid vehicles and

lightening vehicles or with more radical actions that are new to the market, like electrically powered

vehicles. In literature (Ryan, Hosken & Greene, 1992; Wever, Boks & Bakker, 2008; Jabbour et al.,

2015; Yenipazarli & Vakharia, 2017), the general approach to introduce green products is twofold.

3.3.1 Restructuring the product portfolio

The first strategy is referred to with a variety of names such as, redesigned product, greened-up product

and refreshed brown product (Yenipazarli & Vakharia, 2017). This strategy encompasses the

replacement or redesign of an existing brown product for improved environmental performance (Ryan

et al., 1992) by a greener version or alternative. Within this thesis this is referred to as ‘restructured’

products. These brown products are improved or discontinued from the current portfolio as a result of

their inferior performance on sustainability (Wever, Boks, & Bakker, 2008). In order to alter the

attributes of current products and become green(er), the environmental impact of the entire product life

cycle needs to be improved. This entails a reduction or substitution of environmental hazardous

substances (González-Benito & González-Benito, 2006), reduction in the use of energy, water and other

resources, lower carbon-emissions and waste (Lindell & Karagozoglu, 2001) and the implementation of

biodegradable packaging (Kammerer, 2009; Yenipazarli & Vakharia, 2017).

3.3.2 Extending the product portfolio

The second strategy a firm can use in greening its product portfolio is to add completely new green

products that are built from scratch (Yenipazarli & Vakharia, 2017) and based on a new technology

(Ryan et al., 1992) to the existing brown product portfolio. A vital premise within this strategy is that

the green product represents ‘’minimal (if not zero) environmental impact’’ (Yenipazarli & Vakharia,

2017). This is the philosophy of eco-design: Designing new products that minimize the environmental

impact throughout the product’s life (Karlsson & Luttropp, 2006; Jabbour et al., 2018) while ensuring

quality and customer satisfaction. When this strategy is implemented and new green products are added

to the product portfolio, the brown products that already were in the portfolio are continued without any

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changes to them. An implication of this strategy is that the brown products in the portfolio are still

available to the market as before and thereby they keep polluting the environment like before as well.

3.4 Hypotheses

Main Street as a stakeholder is concerned with the society as a whole (Frank, 1988). Given the current

increasing focus on environmentally friendly lifestyles (Neuvonen et al., 2014; Capstick et al., 2015),

they, as a group, want the environment to improve, instead of deteriorating it by the production and use

of brown products. Some customers refuse to buy products that harm the environment (Qi, Shen, Zeng,

& Jorge, 2010; Zeng et al., 2011; Weng, Chen, & Chen, 2015). As a result, companies are encouraged

by Main Street to create green products. Therefore, Main Street is pleased with firms who address the

problem of the polluting (brown) products by restructuring their product portfolios to become greener

by substituting brown products by green environmentally friendly versions of their products. Main Street

rewards firms that green their product portfolio with legitimacy. For these reasons, the following

hypothesis is stated:

H1a: If a firm restructures its product portfolio by substituting brown products for green products, then

Main Street’s perception of legitimacy of the firm will increase.

Restructuring a firm’s portfolio through the discontinuation of good selling brown products as demanded

by the market, brings anticipated, but hard to predict opportunities and consequences for firm

profitability. In some instances, cost savings occur due to more efficient production processes (Hart &

Ahuja, 1996). In others, the product in the restructured portfolio could end up being more expensive

after the brown version has been substituted by a green alternative (Yenipazarli & Vakharia, 2017).

Possible factors causing the price increase after substitution are the costs of the innovation process itself

and more expensive materials. As a result, the market has to pay a price premium for the green product.

This can turn the bright prospect of increasing sales into a rather grim sales prospect, since not all

consumers are willing to pay this premium (Miremadi, Musso, & Weihe, 2012), leading to a drop in

sales and thereby firm profitability. Hence, the green restructuring of the product portfolio brings a lot

of uncertainty considering the sales prospects, while profitability of the prior brown product was

relatively certain. Therefore, it is proposed that:

H1b: If a firm restructures its product portfolio by substituting brown products for green products, then

then Wall Street’s perception of legitimacy of the firm will decrease.

Firms that operate in the market today, might be attacked because there are no green products in their

portfolio. Introducing new green products to the current product portfolio at the hand of the portfolio

extending strategy can be done under a firm’s existing brands or through new brands. For the latter

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scenario, Unruh and Ettenson (2010) have posed that a broader brand portfolio will have a firm more

exposed to activist and consumer backlash. Not to mention that most firms have a lack of green heritage

when they enter the spotlight with their freshly and one (of few) green product. The main reason of this

originates from the fact that many incumbent firms have developed the products in their portfolio before

sustainability was a point of concern. However, a too big imbalance between green and brown products

can undermine a firm’s legitimate sustainability claims (Unruh & Ettenson, 2010). When this occurs,

firms can end up being accused of greenwashing (Delmas & Burbano, 2011). Greenwashing is the act

of positive communication about environmental performance to increase profits (Yadav & Singh, 2014),

whereas in reality having a rather poor environmental performance (Delmas & Burbano, 2011). Wright

(1986) found that acts of corporate social responsibility are discounted when they appear to be motivated

by profit. Alternatively, Ashforth and Gibbs (1990) lay out another theoretical reason why firms that try

to defend their legitimacy become the victim of their own portfolio restructuring strategy and actually

worsen their legitimacy. In the case that a firm is under attack because of its lack of green products and

answers with the introduction of some green products, this can lead to even more harm. Main Street

does notice the new green product, yet it does not see the firm deal with the brown products the firm got

in trouble for. The actual product portfolio of brown products remains the same and these brown

products are kept in the market. Meaning that the negative effects of environmentally unfriendly

products in the portfolio are not addressed, causing a dent in Main Streets judgement of the firm. For

these reasons, the following hypothesis is formulated:

H2a: If a firm extends its product portfolio with additional green products, then Main Street’s

perception of legitimacy of the firm will decrease.

Lamin and Zaheer (2012) noted that Wall Street puts a considerable emphasis on a firm’s future

performance. A manner to eventually satisfy Wall Street, according to a study by Kelm, Narayanan, &

Pinches (1995) is to focus on economic opportunities and environmental risk management. The intention

of utilizing economic opportunities is to achieve superior financial performance. The studies by

Schaltegger and Figge (1997) and Kiernan (2001) have proven this can be done by innovation in green

products. Besides, in the occasion that a firm decides to extend its current brown product portfolio by

complementing it with green products, the firm is still maximizing its returns from existing technologies

(Tushman & Anderson, 1986; Christensen, 1997). As a result, the firm is doing business as usual with

the brown products, but better by answering to the demand for green products. Furthermore, a study by

Kekre and Srinivasan (1990) found that product line broadening leads to a higher market share and

increased profitability. When new green products are added to the brown product portfolio this is product

line broadening, leading to a higher market share. Thus, introducing new green products could lead to

the firm gaining in legitimacy. These observations suggest the following hypothesis:

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H2b: If a firm extends its product portfolio with additional green products, then Wall Street’s perception

of legitimacy of the firm will increase.

4. METHODOLOGY

In this section the methodological decisions in the current research are described. The first section

elaborates on the dataset, sample and time span. Second, the measurement of the dependent,

independent, and control variables of interest are be described. Third, the technique of analysis is

described.

4.1 Sample and data sources

In selecting a sample, it was chosen to focus on firms in the worldwide automotive industry. A new

dataset was constructed by combining these four databases: Fortune, ASSET4, Eikon and OECD.Stat.

Fortune’s yearly ‘World's Most Admired Companies list’, which is also referred to as the Fortune

corporate reputation index (FRI). This list is used as a measurement of the public opinion on a firm over

time. The firms included in the World's Most Admired Companies list are based on the public opinion

and comes from either the FORTUNE 1000 and global 500 lists, which are based on respectively

revenue and revenues of 10 billion or more. The Fortune database comprises 57 separate industry lists.

From the Fortune database the list labeled ‘motor vehicles’ was selected as source list. The scores

(ranging from one to ten) are based on surveys held among industry’s senior executives, directors, and

industry analysts on nine criteria (Appendix A). The ASSET4 database of Thomson Reuters accessible

through their DataStream service which provides environmental, social and governmental (ESG)

information. This database has grown from a worldwide coverage of 1500 firms in 2002 to 7000+ firms

present day. Eikon is a package of software products from Thomson Reuters too. This study used the

Microsoft excel part that provides data on firm specific financial data like ASSET4. The OECD.Stat

database is run by the Organization for Economic Co-operation and Development (OECD). This is an

intergovernmental economic organization consisting of 36 member states. Their goal is to stimulate

economic progress and world trade, of which is kept track in their own database on 22 different themes.

The last source of data consists of publicly available data on products of car manufacturers gathered

online.

From these databases a subset of firms was selected based on their presence in either the FORTUNE

1000 companies or Global 500 companies list and in the ASSET4 database during the years of 2006 and

2019, within this period the FRI scores of the top 19 car manufacturers have been published. From these

19 firms only the firms of which data was available of minimally five years were incorporated in the

dataset, leaving a total of fourteen firms. Finally, the companies where the name of the holding is directly

related to one specific brand name were included in the dataset. Ultimately, this leads to a total sample

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of thirteen multinational companies considered to have a comparable global impact and

knowledgeability.

The data gathered comprises observations of multiple phenomena over multiple time periods for the

same firms, therefore it will be ultimately presented and analyzed as a panel data. As a result of the

merger of the Fortune, Eikon and ASSET4 dataset, the panel data sample of this study consists of thirteen

firms over the time frame of fourteen years (2006-2019), providing a total of 182 observations. The

population under study represents around 64% of the global sales of automobiles (OICA, 2017).

4.2 Measurement of the variables

In constructing the panel data, data is gathered on several different measures for every firm and every

year on December 31st. Two measures related to the product portfolio greening strategies are

implemented and two to reflect the opinion of Main Street and Wall Street on the green strategies. Also,

a few control variables are incorporated in the dataset. Due to endogeneity concerns all the independent

variables are lagged with one year. Resulting from the fact that the dependent variable is measured one

year later in time than the independent variables, the dependent variable can not influence the

independent variables, but the independent variables can influence the dependent variables.

Greening strategies

In greening their product portfolio car manufacturers primarily look for alternative technologies to

power their vehicles (Nunes & Bennett, 2010). Alternative powering of vehicles can be considered a

valid measure of product portfolio greening. Replacing gasoline and diesel for hybrid, fully electric and

in some cases (hydrogen) fuel-cell systems. The adoption of alternative fuels by manufacturers is taken

as a measure for an assessment as greening strategy, given that it is the most direct measure. Data was

collected on models that were introduced and sold in the given time frame and consume alternative fuels.

This measure of product portfolio greening is segregated into two greening strategies. As this measure

of greening strategies is not used priorly, validity will be subject of discussion in this thesis. In order to

segregate the green strategy after its occurrence in the marketplace, the following rationales are leading:

RESTRUCTURING When an alternative fuel vehicle model at the moment of introduction is already

offered in the form of a traditional fuel (diesel and/or gasoline), then the green strategy is subjected to

the category of restructuring, because the portfolio is restructured through modification of an existing

product.

EXTENTION When the alternative vehicle model at the moment of introduction is not present in the

form of a traditional fuel (diesel and/or gasoline), then the green strategy is subjected to the category of

extending, since the portfolio is extended through the introduction of a totally new product.

Based on the data gathered a dummy variable for both strategies was created. The application of the

restructuring strategy in a year is labeled as 1 when it is present and 0 when this strategy is absent.

16

Likewise, the application of the extending strategy in a year is labeled as 1 when it is present and 0 when

it is absent. The overview was created by use of publicly available online trade platforms such as

Gaspedaal.nl, Ebay.com and Cars.com.

Reviewing the sentiment of Main Street

Main Street’s attitude towards the greening strategies in the automotive industry is measured by use of

reputation index data from Fortune. The scores in this range from one (poor) to ten (excellent). Despite

the wide use in academic literature, the list has received the suggestion from Frombrun & Shanely (1990)

and McGuire, Schneeweis, & Branch (1990) of being “an amalgamation of financial metrics that reflects

a firm‘s overall financial health” (Hall & Lee, 2014). However, after research Lee & Hall (2008)

conclude that “the validity of the FRI as an acceptable proxy for firm reputation and social responsibility

has been reestablished”. When a firm’s score has improved (gotten higher) compared the previous year,

this indicates that Main Street approves the greening strategy. This results in Main Street granting

legitimacy to the firm, since the firm has delivered on the interests of them. For the firms included in

the sample, missing values in the dataset were left as a blanc, not meaning a score of zero, but meaning

that the firm was not included in the list of the specific year(s). Not insignificant to mention, is that there

is a lag in creating the list. The list titled Most Admired Companies 2019 is based on the findings of the

book year of 2018. The sentiment of Main Street was checked and controlled for its distribution and

outliers. This variable was complied with the assumptions and no additional corrections have been

performed.

Reviewing the sentiment of Wall Street

The sentiment of Wall Street regarding the strategic choices, is measured by the financial measurement

of the Tobin’s Q. This market based measure illustrates the ratio between a firm’s physical asset market

value and its replacement value. Data that are used as inputs to generate the Tobin’s Q score are retrieved

from ASSET4 of Thomson Reuters DataStream. The current study uses the method of Chung and Pruitt

(1994) to calculate the Tobin’s Q. This method is less sophisticated compared to the more traditional

method of Lindenberg & Ross (1981). Nevertheless, when the financial and accounting data are put

together, the formula by Chung and Pruitt (1994) is highly correlated with Lindenberg and Ross’s

(1981). Following the formula used in the research of Chung and Pruitt (1994), Tobin’s Q is calculated

as1:

Tobin's Q = (MVE + PS + DEBT)/TA

The resulting scores can range from 0 until infinity. However, in most industries the average score will

center around one. A score of one indicates that the market value of a firm’s assets is equal to the book

1 The specific calculations of the factors from the formula can be found in Appendix 2

17

value of its assets. In certain circumstances the assets of a firm are valued higher in the market than their

actual book value, this results in a score higher than one. In other circumstances, it can be that the score

is lower than one, indicating that a firm’s assets are valued lower in the market than their book value.

With this knowledge, the Tobin’s Q is used to check whether the market value of a car manufacturer

has increased compared to the year before implementation of the restructuring and/or extending strategy.

An increase in the Tobin’s Q, and especially above one, implies that there is a positive sentiment with

Wall Street. Indicating that, among others, the selected greening strategy has made the value of the firm

rise over the past year. This results in Wall Street granting legitimacy to the firm, since the firm has

delivered on the interest of them. A decrease in the Tobin’s Q, and especially below one, implies that

there is a negative sentiment with Wall Street. Indicating that, among others, the selected greening

strategy has made the value of the firm lower over the past year. This results in Wall Street denying or

decreasing the legitimacy granted to the firm, since the firm has not delivered on the interest of them.

Not insignificant to mention, is that the Tobin’s Q is a forward looking measure. To illustrate, the ratio

of 2018 reflects Wall Street’s expectations for 2019. The sentiment of Wall Street was checked and

controlled for its distribution and outliers. This variable did not comply with the requirements of a

regression and was therefore winsorized at 0% and 98%.

Control variables

Firm Size

Total assets is used as a measure of firm size. Research from Kemp et al. (2003) found that firm size is

an influencing factor on a firm’s innovation activity and performance. The logic behind this, according

to Tsai, (2001), is that larger firms have more resources at their disposal in order to ameliorate their level

of innovation and performance. However, apart from this benefit, large firms are also more likely to be

under pressure to maintain legitimacy (Meyer & Rowan, 1977). Reason for this is that large companies

are the target of regulators, media, communities and consumers when it comes to environmental

complaints (Guoyou et al., 2013). This makes large firms commercially vulnerable to the judgements

from these stakeholders (Roberts, 1992). The variable Total Assets is checked for its distribution,

outliers and linearity with the dependent variables. The distribution of this variable has a natural

logarithm pattern. Therefore, this variable was log-transformed.

Firm Age

Firm age is used as a measure to check for its influence on the innovation activities of firms (Hansen,

1992). However, there is no clear conclusion on whether an older or younger firm is more innovative.

Ziegler (2014), found that in some instances younger firms are more innovative due to their effort to

increase market share, while in others, older firms are more innovative due to the organizational

resources they have acquired over time. This variable is checked for its distribution, outliers and linearity

18

with the dependent variables. Based on this test the distribution of the variable Firm Age did not need

to be adjusted.

R&D Intensity

Research and development (R&D) intensity is used as a measure of the amount of money a firm spends

on R&D. It is the percentage of a firm’s turnover spend on R&D. A higher percentage could result in a

higher number of green innovations (McWilliams & Siegel, 2000). This could provide more incentive

to Main Street and Wall Street to increase or decrease a firm’s legitimacy based on their desires.

Performance

Return on Assets (ROA) is used as a measure of performance, the relationship between financial and

social performance has been proven in other investigations by among others Dam & Scholtens (2012).

Also, Zahra, Neubaum & Huse (2000) showed that the availability of resources can enhance the

development of innovative and environmental activities. This variable is checked for its distribution,

outliers and linearity with the dependent variables. Based on this test the ROA did not comply with the

requirements of a regression analysis and was therefore winsorized at 6% and 98%.

GDP per Person Employed

GDP per person employed is a measure used as an indication of the size of economies in which the car

manufacturers are head quartered based on the income of the people employed in these countries. Even

though the automotive industry is an international industry ordinarily, a large proportion of its sales take

place in the domestic market. Ordinarily the country with the most sales per head of the population is

the home country, as can be seen with Renault (Renault, 2019) and Mercedes-Benz (Daimler, 2018).

GDP per person employed indicates the disposable income of the population and thereby the opportunity

for them to buy a (domestically produced) vehicle. This variable is checked for its distribution, outliers

and linearity with the dependent variables. The distribution of this variable has a natural logarithm

pattern. Therefore, this variable was log-transformed.

Gov. R&D Inv. Environmental government R&D is a measure used to indicate the attitude of a nation towards

environmental activity. The environmental government R&D is measured as a percentage of the total

government R&D expenditures done on environmental policy. It is shown that when a government

invests in environmental R&D, the innovation increases significantly on a nationwide level (Bai et al.,

2019). This implies that if a car manufacturer’s head quarter is located in a country whose government

invests in environmental R&D, more green products can be expected.

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Free Cash Flow

Free cash flow is a measure used to monitor the slack resources available (Brown et al., 2009). It is

calculated as operating cashflow minus capital expenditures (Bloch, 2005). Literature is not

unambiguous with regard to the effect of free cash flow on innovation. Agency theory claims that slack

resources obstruct innovation activity, while behavioral theory argues that slack resources foster

innovation activity (Lee & Wu, 2016). This variable is checked for its distribution, outliers and linearity

with the dependent variables. Based on this test, the performance variable of this variable did not comply

with the requirements of a regression and was therefore winsorized at 23% and 83%.

Marketing Intensity

Marketing intensity is operationalized via the Selling and general expenses (S&GE) (Mizik et al., 2007;

Luo, 2008; Kurt & Hulland, 2013). This measure is used as a proxy of the marketing efforts made to

communicate and convert a business’s mission to the public. The S&GE measured via Datastream

includes the R&D expenses. Therefore, to create a more accurate measurement of marketing intensity

and following Kurt and Hulland (2013) this study subtracted the R&D expenses from the S&GE

expenses. Marketing activities of a firm are the communication with the outside world. Therefore, both

Main Street and Wall Street may be influenced by the marketing activities. Thereby, higher S&GE

expenditures could indicate that more information is available for both Main Street and Wall Street to

influence their perception.

4.3 Technique of the analysis

The current study used two dependent variables. The first dependent variable is a measure to determine

the sentiment of Main Street. Sentiment was measured by the FRI score published by Fortune. The score

ranges from one to ten with and is measured at a two decimal level. Since this variable has the nature of

an interval/ratio type variable, a standard parametric test could be performed. The second dependent

variable measures the sentiment of Wall Street, which was performed by the Tobin’s Q value. This value

can range from zero to infinity. The Tobin’s Q measurement also has the nature of the interval/ratio type

variable. Therefore, a standard parametric test could be performed here as well.

In this study Stata was used to analyze the data. The data consisted of a repeated measurement of the

same subject (panel data) on the interval/ratio scale. Therefore, a fixed effects regression analysis was

performed. The data was balanced for each and every subject (balanced panel data) on the number of

years. The added value of a fixed effect regression analysis is that it focusses on the structural change

in time. In addition of the assumption of a regression all variables that are influencing the dependent

variable are included in the model. If variables are omitted from the model, the model suffers from

omitted variables bias. The fixed effects regression controls for this unobserved heterogeneity and

corrects for the omitted variable bias.

20

Both dependent variables were inspected for their distribution and outliers, because a regression is

sensitive for influential data points and outliers (Stevens, 1984). The measurement on sentiment of Main

Street complied with the requirements of a regression and is used unaltered. The measurement on

sentiment of Wall Street, however, did not comply with the requirements of a regression and was

therefore winsorized at 0% and 98%. The independent variables in this study are lagging because the

sentiment of Main Street is based on the test results of the previous year. Time alignment of the

dependent and independent variables was performed by lagging the independent variables accordingly.

Since the risk of endogeneity with the sentiment of Main Street variable is low, no additional measures

were necessary. The independent variables and the sentiment of Wall Street could suffer from

endogeneity problems. Causality between the market value of the company and the sentiment of Wall

Street is conceivable. The management of the company could perform certain actions in the expectation

of a higher stock price, leading to causality between the independent variables and the sentiment of Wall

Street. For this reason, the independent variable related to the Wall Street sentiment data are lagged to

reduce the possible reversed causality problems.

The analysis was characterized by the following regression formulas:

Sentiment of Main Streetit = B0 + B1 * Restructureit-1 + B2 * Extendit-1 + B3 * Firm Sizeit-1 + B4 * Firm

Ageit-1 + B5 * R&D Intensityit-1 + B6 * Performanceit-1 + B7 * GDP Employedit-1 + B8 * GOV Env. R&Dit-

1 + B9 * Free Cash Flowit-1 + B10 * Dummy R&D Intensityit-1 + B11 * Marketing Intensityit-1 + εit

Sentiment of Wall Streetit = B0 + B1 * Restructureit-1 + B2 * Extendit-1 + B3 * Firm Sizeit-1 + B4 * Firm

Ageit-1 + B5 * R&D Intensityit-1 + B6 * Performanceit-1 + B7 * GDP Employedit-1 + B8 * GOV Env. R&Dit-

1 + B9 * Free Cash Flowit-1 + B10 * Dummy R&D Intensityit-1 + B11 * Marketing Intensityit-1 + εit

5. RESULTS

5.1 Descriptive statistics and correlations

In table 1 and table 2 the sample statistics are respectively presented for the available observations of

the dependent variables of Sentiment Main Street and Sentiment Wall Street. The presented sample

statistics include the mean, standard deviation, minimum and maximum of all variables incorporated in

this research. Additionally, the dummy variable introduced for the R&D intensity variable is shown.

Descriptive statistics for Main Street analysis

From table 1 the mean and standard deviation of Sentiment of Main Street could be seen (M = 5.675,

SD = 1.174). As a number can be considered as a grade on a ten point scale, the used sample of

automotive industry has received a sufficient score. Firm Size (M = 21.038, SD = 2.519) which indicates

21

the firms on average have 1369895094 dollars’ worth of assets. Firm Age (M = 83.483, SD = 18.229)

show that the firms are on average over three quarters of a century old. From this it could derived that

they are well established. R&D Intensity (M = 2.266, SD = 2.113) shows that the average investment in

R&D is 2.266% of the total revenue. Performance (M = 3.532, SD = 1.769) determined by the ROA,

shows that the net income is 3.567 dollars per one dollar in assets under the control of the firms. GDP

Employed (M = 11.344, SD = 0.153) is relatively high (Worldbank, 2019). Gov. R&D Inv. (M = 2.041,

SD = 0.978) presents that 2.041% of the R&D investments the governments of the head quarter based

countries is spend on environmental R&D. Free Cash Flow (M = -9.88e+08, SD = 2.44e+10) indicates

that the average cash flow is negative with -9.88e+08.

Descriptive statistics for Wall Street analysis

From table 2 the following descriptive statistics on the mean and standard deviation are derived on the

Sentiment of Wall Street (M = 0.573, SD = 0.165). This indicates that automotive industry in this sample

has a Tobin’s Q score of 0.573. This is a normal score for this industry (Khoo, 2019). Based on research

of Khoo (2019) the Tobin’s Q for a car manufacturer such as Honda varies between 0.2784 and 0.4462.

Firm Size (M = 20.894, SD = 2.401) which indicates the firms on average have 1186175378 dollars’

worth of assets. Firm Age (M = 87.497, SD = 18.998) show that the firms are on average over three

quarters of a century old. From this it could be derived that they are well established. R&D Intensity (M

= 2.259, SD = 2.055) shows that the average investment in R&D is 2.259% of the total revenue.

Performance (M = 3.477, SD = 1.816) determined by the ROA, shows that every dollar the firm has put

in assets has a return of 3.477 dollars. GDP Employed (M = 11.347, SD = 0.147) is relatively high

according to the Worldbank (2019). Gov. R&D Inv. (M = 2.072, SD = 0.933) presents that 2.072% of

the R&D investments the governments of the head quarter based countries is spend on environmental

R&D. Free Cash Flow (M = 3.46e+09, SD = 2.55e+10) indicates that the average cash flow is positive

with 3.46e+09.

The descriptive statistics of the firms in Sentiment of Main Street variable are based on a total of 118

observations and the descriptive statistics of the firms in Sentiment of Wall Street variable are based on

a total of 159 observations. The difference in observations can be explained. As explained in the

methodology, only the top 15 is published. Therefore, there is no data available for some firms in certain

years, leading to the exclusion for these firms.

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TABLE 1: Descriptive statistics - Sentiment of Main Street

Variables Mean Std. Dev Min Max

Sentiment Main Street 5.675 1.174 3.200 7.990

Restructuring 0.271 0.446 0 1

Extending 0.093 0.292 0 1

Firm Size (ln) 21.038 2.519 17.849 25.827

Firm Age 83.483 18.229 38 117

R&D Intensity 2.266 2.113 0 6.540

Performance (wins) 3.532 1.769 0.070 7.710

GDP Employed (ln) 11.344 0.153 10.974 11.725

Gov. R&D Inv. 2.041 0.978 0.385 4.124

Free Cash Flow (wins) -9.88e+08 2.44e+10 -3.07e+10 4.51e+10

Dummy R&D Intensity 0.390 0.490 0 1

Marketing Intensity 0.108 0.035 0.032 0.187

Number of observations: 118; Std. Dev: standard deviation; Min: minimum; Max: maximum

TABLE 2: Descriptive statistics - Sentiment of Wall Street

Variables Mean Std. Dev Min Max

Sentiment Wall Street 0.573 0.165 0.225 1.136

Restructuring 0.239 0.428 0 1

Extending 0.107 0.310 0 1

Firm Size (ln) 20.894 2.401 17.849 25.903

Firm Age 87.497 18.998 38 118

R&D Intensity 2.259 2.055 0 6.540

Performance (wins) 3.477 1.816 0.070 7.710

GDP Employed (ln) 11.347 0.147 10.974 11.711

Gov. R&D Inv. 2.072 0.933 0.397 4.124

Free Cash Flow (wins) 3.46e+09 2.55e+10

-

3.07e+10 4.51e+10

Dummy R&D Intensity 0.365 0.483 0 1

Marketing Intensity 0.111 0.035 0.032 0.187

Number of observations: 159; Std. Dev: standard deviation; Min: minimum; Max: maximum

The correlation matrix in table 3 and table 4 show respectively the correlations of the dependent

variables Sentiment of Main Street and Sentiment of Wall Street.

Table 3 shows a positive insignificant correlation between restructuring and Sentiment of Main Street

(r = .090, p = .334). This indicates that there is a very weak positive association between Restructuring

and Sentiment of Main Street (Ahmad & Usop, 2011). Although it was expected that this correlation

would have been stronger, this is in line with the theoretical expectations as discussed in the theoretical

framework. Table 3 shows a positive significant correlation between Extending and Sentiment of Main

Street (r = .183, p = .047). This indicates that there is a very weak positive association between

23

Extending and Sentiment of Main Street (Ahmad & Usop, 2011). Admitting it is only a weak correlation,

this is not in line with the theoretical expectations discussed in the theoretical framework.

Table 4 shows a negative significant correlation between Restructuring and Sentiment of Wall Street (r

= -.030, p = .705). This indicates that there is a strong negative association between Restructuring and

Sentiment of Wall Street. This is in line with the theoretical expectations as discussed in the theoretical

framework. Table 4 shows a positive insignificant correlation between Extending and Sentiment of Wall

Street (r = .082, p = -.304). Although this indicates that there is a weak positive association between

Extending and Sentiment of Wall Street. This is not in line with the theoretical expectations as discussed

in the theoretical framework.

25

TABLE 3: Correlation Table - Sentiment Main Street

Variables 1 2 3 4 5 6 7 8 9 10 11 12

1. Sentiment Main Street 1.000

2. Restructuring 0.090 1.000

(0.334)

3. Extending 0.183 0.067 1.000

(0.047) (0.473)

4. Firm Size 0.050 0.019 0.184 1.000

(0.587) (0.837) (0.046)

5. Firm Age -0.072 -0.050 -0.087 -0.744 1.000

(0.437) (0.592) (0.348) (0.000)

6. R&D Intensity 0.099 -0.200 0.138 0.112 -0.071 1.000

(0.289) (0.030) (0.136) (0.226) (0.447)

7. Performance 0.361 0.084 -0.013 0.065 -0.057 0.002 1.000

(0.000) (0.368) (0.885) (0.487) (0.538) (0.983)

8. GDP Employed -0.003 0.010 -0.140 -0.799 0.799 -0.031 0.050 1.000

(0.972) (0.913) (0.130) (0.000) (0.000) (0.737) (0.591)

9. Gov. R&D Inv. 0.268 0.087 0.025 -0.212 -0.113 -0.076 0.013 -0.084 1.000

(0.003) (0.350) (0.792) (0.021) (0.223) (0.412) (0.888) (0.366)

10. Free Cash Flow -0.039 -0.054 -0.174 -0.252 0.111 -0.102 0.287 0.163 0.056 1.000

(0.672) (0.562) (0.059) (0.006) (0.232) (0.270) (0.002) (0.078) (0.548) 11. Dummy R&D Intensity -0.010 0.177 -0.077 -0.213 0.225 -0.861 -0.065 0.194 -0.041 0.038 1.000

(0.919) (0.055) (0.407) (0.020) (0.014) (0.000) (0.487) (0.035) (0.662) (0.683) 12. Marketing Intensity -0.336 -0.137 -0.108 0.043 -0.261 0.212 0.030 -0.298 -0.117 0.283 -0.248 1.000

(0.000) (0.138) (0.242) (0.643) (0.004) (0.021) (0.748) (0.001) (0.206) (0.002) (0.007)

Number of observations: 118. Values between parentheses are the p-values

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TABLE 4: Correlation Table - Sentiment Wall Street

Variables 1 2 3 4 5 6 7 8 9 10 11 12

1. Sentiment Wall Street 1.000

2. Restructuring -0.030 1.000

(0.705)

3. Extending 0.082 0.045 1.000

(0.304) (0.576)

4. Firm Size (ln) 0.013 0.054 0.198 1.000

(0.869) (0.502) (0.012)

5. Firm Age -0.183 -0.082 -0.137 -0.724 1.000

(0.021) (0.306) (0.085) (0.000)

6. R&D Intensity 0.380 -0.142 0.083 0.053 -0.108 1.000

(0.000) (0.074) (0.299) (0.505) (0.177)

7. Performance 0.142 0.095 0.013 0.086 -0.052 -0.004 1.000

(0.074) (0.232) (0.866) (0.278) (0.519) (0.961)

8. GDP Employed -0.032 -0.034 -0.099 -0.801 0.673 0.025 0.045 1.000

(0.688) (0.671) (0.213) (0.000) (0.000) (0.751) (0.575)

9. Gov. R&D Inv. -0.082 0.016 0.010 -0.182 -0.039 -0.080 0.022 -0.087 1.000

(0.302) (0.842) (0.903) (0.021) (0.628) (0.316) (0.779) (0.273) 10. Free Cash Flow 0.000 -0.014 -0.050 -0.106 0.145 -0.139 0.298 0.018 0.072 1.000

(0.998) (0.862) (0.528) (0.183) (0.068) (0.080) (0.000) (0.821) (0.369) 11. Dummy R&D Intensity -0.239 0.157 -0.051 -0.100 0.156 -0.835 -0.019 0.090 -0.081 0.077 1.000

(0.002) (0.048) (0.525) (0.211) (0.050) (0.000) (0.817) (0.261) (0.312) (0.337) 12. Marketing Intensity 0.204 -0.060 -0.070 0.125 -0.168 0.171 0.075 -0.360 -0.100 0.385 -0.121 1.000

(0.010) (0.455) (0.383) (0.116) (0.034) (0.031) (0.347) (0.000) (0.210) (0.000) (0.128)

Number of observations: 159. Values between parentheses are the p-values

27

Correlations Main Street

When the correlations between the independent variables have a correlation of .80 or higher,

multicollinearity could be present (Bryman and Cramer, 1997). As shown in table 3 and 4 there were no

high correlations. However, there are some other high correlations. For the variable of Sentiment of

Main Street, there are strong correlations between Firm Size and Restructuring (r = 0.019, p = .837),

Performance and Extending (r= -.013, p= .885), Gov. R&D Inv. and Extending (r = .025, p = .792),

Gov. R&D Inv. and R&D Intensity (r = .013, p = .888) and between Marketing Intensity and

Performance (r = .030, p = .748). Furthermore, there were very strong correlations between Performance

and R&D Intensity (r = 002., p = .983), GDP Employed and Sentiment Main Street (r = -.003, p = .972),

GDP Employed and Restructuring (r = .010, p = .913) and between the Dummy R&D Intensity and

Sentiment Main Street (r = -.010, p = .919). Therefore, a variance inflation factor inspection is

performed. The highest VIF value found between Firm Size and GDP Employed in the regression

analysis is Firm Size with a value of 6.40. This high VIF value may be caused by the strong correlation

between Firm Size and GDP Employed. This suggests that the larger car manufacturers are located in

more wealthy countries. However, this study has not formulated a hypothesis with regard to these

variables. Therefore, the high VIF value is of no consequence for the hypotheses tested. Craney & Surles

(2002) advice a cut-off point of 10, so in this sample there is no indication for multicollinearity.

Correlations Wall Street

For the variable of Sentiment of Wall Street, there are strong correlations between Firm Size and

Sentiment Wall Street (r = .013, p = .869), performance and extending (r = .013, p = .866), GDP

Employed and R&D Intensity (r = .025, p = .751), Gov. R&D Inv. and Restructuring (r = .016, p =

.842), Gov. R&D Inv. and Extending (r = .010, p = .903), Gov. R&D Inv. and Performance (r = .022, p

= .779), Free Cash Flow and Restructuring (r = -.014, p = .862), Free Cash Flow and GDP Employed (r

= .018, p = .821) and between the Dummy R&D Intensity and Performance (r = -.019, p = .817). Besides,

there were very strong correlations between Performance and R&D Intensity (r = -.004, p = .961) and

between Free Cash Flow and Sentiment of Wall Street (r = .000, p = .998). For this reason, a variance

inflation factor inspection is performed. The highest VIF value found between Firm Size and GDP

Employed in the regression analysis is Firm Size with a value of 5.73. This high VIF value may be

caused by the strong correlation between firm size and GDP Employed. This presupposes that the larger

car manufacturers are located in more affluent countries. However, this study has not formulated a

hypothesis with regard to these variables. Therefore, the high VIF value is of no consequence for the

hypotheses tested. Furthermore, this value does not come near the advised cut-off point of 10 set by

Craney & Surles (2002). Therefore, there is no indication for multicollinearity in this sample.

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5.2 Regression results

Table 5 presents the results of the fixed effects regression analysis performed with Sentiment of Main

Street and Sentiment of Wall Street as dependent variables.

Sentiment Main Street analysis

In model 1 there was a negative not significant relation between restructuring and the sentiment of Main

Street (B = -0.050, p = .733). This indicates that the green strategy of Restructuring of the product

portfolio has no effect on the Sentiment of Main Street. This leads to the rejection of hypothesis 1a. In

model 1 there was a positive insignificant relation between Extending and the Sentiment of Main Street

(B = 0.019, p = .920). This demonstrates that the green strategy of product portfolio Extending has no

effect on the Sentiment of Main Street. This leads to the rejection of hypothesis 2a.

In model 1 there was a positive significant relation between Firm Size and the Sentiment of Main Street

(B = 1.289, p < .001). This indicates that larger firms receive more legitimacy from Main Street. Firm

Age had a negative significant effect on the Sentiment of Main Street (B = -0.117, p < .001). This

indicates that the general population receives older firms in general as less innovative in the area of

product portfolio greening. Performance has a positive significant effect on Sentiment of Main Street

(B = 0.212, p < .001). Meaning that profitable firms have their processes under control, efficiency, happy

people. The following variables had no significant effect on the variable of Main Street, R&D Intensity

(B = 0.057, p =.755), GDP Employed (B = 7.553, p = .107), Gov. R&D Inv. (B = 0.221, p = .131), Free

Cash Flow (B = -0.000, p = .172), Dummy R&D Intensity (B = -0.177, p = .854) and Marketing Intensity

(B = -6.312, p = .041).

Sentiment Wall Street analysis

In model 2 there is a positive insignificant relation between Restructuring and the Sentiment of Wall

Street (B = 0.006, p = .674). This demonstrates that the green strategy of product portfolio Extending

has no effect on the Sentiment of Wall Street. Therefore, hypothesis 1b is not confirmed. In model 2

there is a positive insignificant relation between Extending and the Sentiment of Wall Street (B = 0.008,

p = .734). This indicates that the green strategy of product portfolio Extending has no effect on the

Sentiment of Wall Street. Therefore, no supporting evidence is found for hypothesis 2b.

In model 2 there was a positive significant relation between R&D Intensity and the Sentiment of Wall

Street (B = 0.063, p = 0.001). This can be explained by the fact that Wall Street is interested in new cash

flows resulting from innovations, since this ensures the viability of the firm on the long-term. Likewise,

the Dummy Variable of R&D Intensity is positively significant related to Sentiment Wall Street (B =

0.260, p = 0.003). This indicates that firms that do not invest in R&D are positively perceived by Wall

Street. The Performance of car manufacturers has a positive effect on the Sentiment of Wall Street (B =

0.018, p = 0.003). This indicates that the performance of a car manufacturer is not the most important

29

consideration of Wall Street. The following variables had no significant effect on the variable of

Sentiment of Wall Street, Firm Size (B = 0.019, p = .845), Firm Age (B = 0.000, p = 0.998), GDP

Employed (B = -1.246, p = .027), Gov. R&D Inv. (B = -0.019, p = .355), Free Cash Flow (B = 0.000, p

= .710) and Marketing Intensity (B = 1.011, p = .193)

All the variables are insignificant. Because of this no statistical statements can be made about the

Sentiment of Main Street and the Sentiment of Wall Street. To clear up the insignificance of the

hypotheses, possible explanations and additional insights are debated in the discussion.

30

TABEL 5: Fixed Effects Regression

Dependent variable: Sentiment Main Street Sentiment Wall Street

Model 1 Model 2

Restructuring -0.050 0.006

(0.144) (0.014)

Extending 0.019 0.008

(0.188) (0.022)

Firm Size 1.289*** 0.019

(0.171) (0.093)

Firm Age -0.117*** 0.000

(0.022) (0.006)

R&D Intensity 0.057 0.063***

(0.178) (0.015)

Performance 0.212*** 0.018**

(0.039) (0.005)

GDP Employed 7.553 -1.246*

(4.340) (0.495)

Gov. R&D Inv. 0.221 -0.019

(0.136) (0.020)

Free Cash Flow -0.000 0.000

(0.000) (0.000)

Dummy R&D Intensity -0.177 0.260**

(0.942) (0.069)

Marketing Intensity -6.312* 1.011

(2.753) (0.734)

Constant -97.973† 13.939*

(50.300) (5.157)

Observations 118 159

R-squared (within) 0.445 0.210

R-squared (between) 0.064 0.018

R-squared (overall) 0.021 0.027

Highest VIF 6.400 5.730

Number of firms 13 13

Robust standard errors in parentheses. *** p < 0.001, ** p < 0.01, * p < 0.05, † p < 0.1

31

6. DISCUSSION

In the past two decades the automotive industry has experienced the emergence of alternative fuel

vehicles. The underlying motivation for most companies is to gain in legitimacy and competitiveness

and to become ecological responsible (Pratima Bansal & Roth, 2000). While car manufacturing

companies invest billions (Bos & Hsu, 2019) in the development of green strategies, this research is the

first to investigate and answer the question whether product portfolio greening strategies of restructuring

and extending strategies lead to an improvement of the legitimacy granted by the stakeholders of Main

Street and Wall Street. In the current section, the theoretical implications, managerial implications and

an overall conclusion of the results are given. Furthermore, a discussion of the limitations and advice

for future research is presented.

6.1 Theoretical implications

This research started off with the objective to fill the gap in literature on whether legitimacy could be

derived from stakeholders by implementing green product portfolio strategies. The information on this

topic is, if available, minimal with regard to the existing green strategies for auto manufacturers to

enhance their legitimacy. This research contributes to the existing literature on greening strategies in

the automotive industry.

First, it was examined whether the restructuring of the product portfolio enhances the legitimacy

received by car manufacturers from Main Street (H1a). Non-shareholders value a firm’s actions from

the perspective of the broader societal impact (Frank, 1988). Accordingly, it was hypothesized that

firms could achieve legitimacy from Main Street by the restructuring of the product portfolio.

However, the current research did not provide evidence that restructuring does lead to the rewarding

with legitimacy from Mainstreet. A possible explanation of the absence of an effect of restructuring on

Main Street legitimacy may be found in the unchanged production of brown products. As Ashforth

and Gibbs (1990) posed, Main Street is able to notice when car manufacturers introduce a new green

product, but does not deal with the existing brown products in the meantime. The imbalance between

green and brown products undermines a firm’s legitimate sustainability claims (Unruh & Ettenson,

2010). Which implies that the greening efforts of car manufacturers do not change the perception of

Main Street with regard to sustainability/greening and therefore do not grant any additional legitimacy

to the restructuring car manufacturing. However, as long as there is no accusation of greenwashing

against the firm, the legitimacy does not worsen either.

Second, it was examined whether the implementation of the restructuring strategy affects the legitimacy

granted to car manufacturers by Wall Street negatively (H1b). Reason for this hypothesis is that Wall

Street would disapprove of product portfolio restructuring, in view of possible price increases for

consumers (Yenipazarli & Vakharia, 2017) and the fact that not all consumers are willing to pay the

32

premium green for vehicles (Miremadi et al., 2012). This can turn the bright prospect of increasing cash

flows into a rather grim sales prospect. Consequently, it is expected that Wall Street perceives the

greening of the product portfolio of car manufacturers as an underdeveloped area that has only limited

profit potential at this moment in time. Notwithstanding, this research does not provide evidence for this

rationale and the negative effect, nor does it prove restructuring to be of a positive effect. Therefore, if

it is taken in consideration that shareholders evaluate a firm based on the future cash flows (approx. five

years), the results may be explained by Wall Street expecting only limited profit potential. Wall Street

may assume that either green cars are not profitable in the coming years or the number of green vehicles

sold will be too low resulting in no response to the restructuring of the product portfolio.

Third, it was examined whether the implementation of the extending strategy had a negative effect on

the legitimacy rewarded by Main Street to car manufacturers (H2a). It was argued that car manufacturers

add green products next to the existing brown products in their product portfolio to prevent being

targeted because of a lack of green vehicles in their offering. Based on research of Unruh and Ettenson

(2010) it was expected that a broader brand portfolio would have firms more exposed to activists and

consumer backlash and undermining of a firm’s legitimate sustainability claims. Ultimately leading to

the accusation of greenwashing (Delmas & Burbano, 2011), owing to the fact that the firm is not taking

action concerning the brown products in the product portfolio (Ashforth & Gibbs, 1990). However, no

evidence was found on the stance that the extending strategy decreases or increases the legitimacy

granted. This shows that superficial greening efforts do not have a relevant effect on the sentiment of

Main Street. Which implies that the greening efforts of car manufacturers do not change the perception

of Main Street with regard to sustainability/greening and therefore do not grant any additional legitimacy

to the restructuring car manufacturing.

Fourth, it was examined whether the implementation of the extending strategy rewards car

manufacturers with legitimacy from Wall Street (H2b). The plea for this statement was that Tushman

and Anderson (1986) and Christensen (1997) showed that complementing the existing product portfolio

with green products enables firms to maximise their returns on existing technologies. This increase of

the future performance is Wall Street’s main interest (Lamin & Zaheer, 2012). However, the current

research found no evidence that extending the product portfolio leads to an increase in the legitimacy

rewarded by Mainstreet. Vice versa, no negative relation was measured either. It seems as if Wall Street

perceives the greening of the product portfolio of car manufacturers as an underdeveloped area that has

only limited profit potential at this moment in time. Therefore, if it is taken in consideration that

shareholders evaluate a firm based on the future cash flows (approx. five years) Wall Street Assumes

that green vehicles may not be profitable enough in the coming years or that the number of green vehicles

sold is too low. Therefore, in their perspective there may only be limited profit potential. Because of

this, Wall Street probably does not respond to the restructuring of the product portfolio.

33

6.2 Managerial implications

Besides the interest of extending the literature, an important motivation in the writing of this paper was

to provide insights to the product portfolio management problem. Especially considering the

enhancement of corporate legitimacy by the implementation of green product portfolio strategies. In

building this research, managerial implications have progressively arisen. The knowledge aggregated

throughout this research could be advantageous for managers in the automotive industry, but more

specifically industry managers who operate in the countries from which the firms were studied in the

sample.

Given the increased attention on environmental sustainability in the last two decades it can be considered

sensible for car manufacturers to focus on greening the product portfolio. However, a positive or

negative effect on granted legitimacy is not proven. The results do show a positive effect on granted

legitimacy from Main Street for younger and profitable firms with intensive marketing. This would

imply that a holding can best start a new brand in order to get rid of the, generally static and/or polarized

view of the existing brand.

With regard to the sentiment of Wall Street, the results show a positive effect on granted legitimacy for

performance and R&D intensity. This finding, combined with the absence of an effect of greening

strategy on legitimacy granted by Wall Street, suggests that Wall Street does not expect a significant

improvement of product portfolio greening on the cash flow.

In summary, the advice to product portfolio managers in the automotive industry is to focus the R&D

research on a new, green brand such that the firm is ready to introduce the new brand as soon as the

automotive industry is shifting towards green vehicles.

6.3 Conclusion

The aim of this research was to explore the effect of different product portfolio greening strategies on

the legitimacy granted by stakeholders in the automotive industry. At the foundation of this is the trend

of customers demanding green products (Randall, 2016) and the answer of car manufacturers in a

varying array of strategies. The legitimacy granted to a car manufacturer is dependent on the sentiments

of the stakeholders of Main Street and Wall Street. The research into the effect of greening the portfolio

on legitimacy granted by stakeholders is an understudied area in the literature.

The results of this study show that both Main Street and Wall Street do not attach great importance to

the implementation of both the restructuring and extending greening strategies. This implies that there

is no gain in legitimacy for car manufacturers in it. From the regression results it can be concluded that

young (B = -0.117, p < .001), large (B = 1.289, p < .001) and profitable (B = 0.212, p < .001) firms that

let their products speak for themselves (low marketing intensity) receive legitimacy from Main Street

because of the authenticity of the greenness they stand for. Firms from that are profitable (B = 0.018, p

= 0.003) and invest a very limited amount in R&D (B= 0.063, p = 0.001 and B = 0.260, p = 0.003 for

34

the dummy variable) are valued by Wall Street. Unexpectedly, the restructuring and extending strategies

have no effect on the sentiment of both Main Street and Wall Street, which indicates that the average

consumer and shareholder do not appreciate the greening of the portfolio of car manufacturers. In

summary, Main Street is mainly interested in large profitable firms that let their products speak for

themselves and Wall Street is mainly interested in countries with profitable firms. These results suggest

that the partial greening of the product portfolio, irrespective of the applied strategy, is not rewarded

with legitimacy by both Main Street and Wall Street. This data suggests that Main Street and Wall Street

have a static or polarized view that car manufacturers are either green or brown.

6.4 Limitations and future research

In order to make future research aware of limitations, they will be provided here.

First, this research makes use of a small dataset with a limited statistical power. Therefore, only large

effects could be detected by this study. That this study did not find any effect of the restructuring and

extending on Main Street and Wall Street implies that these effects may be small and therefore beyond

the detection possibilities of this dataset. There are two possible approaches that could be adopted to

address the problem of a small sample size. The first and, in terms of success chances, most attractive

approach is to use another proxy for the measurement of Main Street sentiment. This study used data

from the FRI to measure the sentiment of Main Street. A more appropriate data source would be one

that provides more data than only provided data on the top 15 car manufacturing firms per year.

Furthermore, only FRI data of firms represented by the holding brand names with one specific main

firm is used. To illustrate, General Motors as a holding does not have one leading brand. Whereas Toyota

as a holding is represented by Toyota. Reason for this is that data is measured by interviewing people.

To ensure the scores are based on complete knowledge, holdings that were not represented by a main

firm were removed from the sample. Thus, the way data is gathered and provided by Fortune led to a

select group of firms to represent the automotive industry in this sample. A second approach is to work

with an international team, such that the currently encountered language barriers disappear and data

from a wider range of countries could be incorporated. This originates from the reality that the

automotive industry is global, and some brands are active in a restricted area (shanghai motors, dong

feng motors), leading to a lack of data in the English language.

Similarly, the measurement of the Wall Street data can be improved. Currently, the Tobin’s Q is

determined using the financial data at the holding level. As a first exploration of the topic this is a fine

choice, since it provided this study with a market based measure for the sentiment of Wall Street.

However, because data on the holding was used, it provided data on groups of firms instead of a specific

firm. A better, but more time-consuming method is to gather the financial data from the annual reports

per firm instead of using the financial data per brand.

35

Within this study a restricted amount of methods used by car manufacturers in greening their portfolio

was used. It was chosen to focus only on alternative (green) fuels. However, there are more methods

available to car manufacturers that may be related to greening their portfolio. These are vehicle down-

sizing (weight and/or size reduction), phasing out of carbon intensive fuel (diesel) vehicles, emission

reductions (adoption of cleaner fossil fuel engines) and the adoption of cleaner fossil fuels (compressed

natural gas (CNG) or liquefied petroleum gas (LPG)). The current measurement was chosen because of

the researchers gut-feeling that alternative fuels are best acknowledged by the public as a green approach

of the company. Integrating these alternative methods into research creates important avenues for future

research as it may lead to a more precise measurement of the greening strategy of the company. In

addition, the proxy used for greening and restructuring via a binary variable limits the detection

possibilities of this study. Therefore, other researchers may be able to detect the effect of restructuring

and extending by using a larger dataset and developing a more accurate proxy of restructuring and

extending.

A possible approach in future research could be to first determine what aspects of the greening strategy

are perceived by the Main Street and chose a measurement based on companies adjustment to this

variable.

36

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APPENDICES

Appendix A: criteria surveyed in FRI survey

1) Quality of management

2) Quality of product

3) Innovativeness

4) Effective use of assets

5) Financial soundness

6) Employee talent

7) Social responsibility

8) Long-term investment value

9) Effectiveness in doing business globally

Appendix B: Factors and their calculation used in the formula to calculate Tobin’s Q

MVE = (Closing price of share at the end of the financial year) * (Number of common shares

outstanding)

PS = Liquidating value of the firm's outstanding preferred stock

DEBT = (Current liabilities - Current assets) + (Book value of inventories) + (Long term debt)

TA = Book value of total assets

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